A qualitative study of hospital and community providers’ experiences with digitalization to facilitate hospital-to-home transitions during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has triggered substantial changes to the healthcare context, including the rapid adoption of digital health to facilitate hospital-to-home transitions. This study aimed to: i) explore the experiences of hospital and community providers with delivering transitional care during the COVID-19 pandemic; ii) understand how rapid digitalization in healthcare has helped or hindered hospital-to-home transitions during the COVID-19 pandemic; and, iii) explore expectations of which elements of technology use may be sustained post-pandemic. METHODS: Using a pragmatic qualitative descriptive approach, remote interviews with healthcare providers involved in hospital-to-home transitions in Ontario, Canada, were conducted. Interviews were analyzed using a team-based rapid qualitative analysis approach to generate timely results. Visual summary maps displaying key concepts/ideas were created for each interview and revised based on input from multiple team members. Maps that displayed similar concepts were then combined to create a final map, forming the themes and subthemes. RESULTS: Sixteen healthcare providers participated, of which 11 worked in a hospital, and five worked in a community setting. COVID-19 was reported to have profoundly impacted healthcare providers, patients, and their caregivers and influenced the communication processes. There were several noted opportunities for technology to support transitions. INTERPRETATION: Several challenges with technology use were highlighted, which could impact post-pandemic sustainability. However, the perceived opportunities for technology in supporting transitions indicate the need to investigate the optimal role of technology in the transition workflow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".